Abstract
Social networks have many advantages and they are very popular. The number of people having at least one account on a certain social network has grown considerably. Social networks allow people to connect and interact more easily with one another, leading to a much easier way to obtain information. However one major disadvantage of social networks is that some information may be untrue. In this paper we propose a protocol in which the network becomes more immune to the diffusion of false information. Our approach is based on evidence theory with Dempster-Shafer and Yager’s rule which plays an important role in an individual’s decision whether to send further the received information or not. We also took into consideration the confidence degree of the neighbours regarding the information which is spread by a specific source node. Furthermore, we propose a simulation algorithm that allows us to observe the diffusion of two contradictory information spread by two different source nodes. The experimental results show that the true information spreads more easily if the ground truth is sometimes revealed, even rarely.
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Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Yager, R.: On the Dempster-Shafer framework and new combination rules. Inf. Sci. 41, 93–137 (1987)
Dong, X.L., et al.: Knowledge-based trust: estimating the trustworthiness of web sources. In: Li, C., Markl, V. (eds.) Proceedings of the VLDB Endowment, vol. 8, pp. 938–949. VLDB Endowment (2015)
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G., Previti, M.: Post sharing-based credibility network for social network. In: Ivanović, M., Bădică, C., Dix, J., Jovanović, Z., Malgeri, M., Savić, M. (eds.) IDC 2017. SCI, vol. 737, pp. 149–158. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66379-1_14
Ahmed, M., Huang, X., Sharma, D.: Dempster-Shafer theory to identify insider attacker in wireless sensor network. In: Park, J.J., Zomaya, A., Yeo, S.-S., Sahni, S. (eds.) NPC 2012. LNCS, vol. 7513, pp. 94–100. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35606-3_11
Canini, K.R., Suh, B., Pirolli, P.L.: Finding credible information sources in social networks based on content and social structure. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, Boston, vol. 1, pp. 1–8 (2011)
Kumar, K.P.K., Geethakumari, G.: Detecting misinformation in online social networks using cognitive psychology. Hum. Centric Comput. Inf. Sci. (2014). 13673
Amoruso, M., Anello, D., Auletta, V., Ferraioli, D.: Contrasting the spread of misinformation in online social networks. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems AAMAS 2017, pp. 1323–1331. International Foundation for Autonomous Agents and Multiagent Systems Richland, São Paulo (2017)
Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, PSOSM 2012. ACM New York, Lyon (2012)
Abbasi, M.-A., Liu, H.: Measuring user credibility in social media. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 441–448. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37210-0_48
Li, R., Suh, A.: Factors influencing information credibility on social media platforms: evidence from Facebook pages. Procedia Comput. Sci. 72, 314–328 (2015)
Liu, Q., Wu, S., Yu, F., Wang, L., Tan, T.: ICE: information credibility evaluation on social media via representation learning. arXiv preprint (2016). https://arxiv.org/pdf/1609.09226.pdf
Muharemi, F., Logofătu, D., Andersson, C., Leon, F.: Approaches to building a detection model for water quality: a case study. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems. SCI, vol. 769, pp. 173–183. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76081-0_15
Curteanu, S., Leon, F., Lupu, A.S., Floria, S.A, Logofatu, D.: An evaluation of regression algorithms performance for the chemical process of naphthalene sublimation. In: Proceedings of the 14th International Conference on Artificial Intelligence Applications and Innovations (AIAI), pp. 219–230 (2018)
Balabanov, K., Logofatu, D., Badica, C., Leon, F.: A simulation-based analysis of interdependent populations in a dynamic ecological environment. In: Proceedings of the 14th International Conference on Artificial Intelligence Applications and Innovations (AIAI), pp. 437–448 (2018)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn., p. 863. Pearson Education, Inc., Upper Saddle River (2010)
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Floria, SA., Leon, F., Logofătu, D. (2018). A Credibility-Based Analysis of Information Diffusion in Social Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_80
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DOI: https://doi.org/10.1007/978-3-030-01424-7_80
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